Apparatus for multi-angle screen coverage analysis

ABSTRACT

Embodiments provide multi-angle screen coverage analysis. In some embodiments, a system obtains a computer graphics generated image having at least one target object for analysis. The system determines screen coverage information and depth information for the at least one target object. The system then determines an asset detail level for the at least one target object based on the screen coverage information and the depth information. The system then stores the asset detail level in a database, and makes the asset detail level available to users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication No. 62/968,047, entitled “APPARATUS FOR MULTI-ANGLE SCREENCOVERAGE ANALYSIS,” filed Jan. 30, 2020, which is hereby incorporated byreference as if set forth in full in this application for all purposes.

BACKGROUND

During visual productions such as movies, videos, etc., conventionalindustry solutions include finalizing layouts and cameras beforecommencing construction of objects used in digital images. Suchconstruction may involve animation and/or lighting work, for example.Waiting for final layouts and cameras can lead to overall delays in avisual production. One approach is to build new and unique objects andsets for each shot. That would deliver the most appropriatebuild-quality in that shot. However, the cost is the inability tocapitalize on that effort by reusing the objects in other shots. Asecond approach is to build all objects to highest-possible quality.However, this risks expending wasted effort during asset creation thatwill make no impact on the final images. Or, worse yet, it risksnegatively impacting a system's ability to create images on a technicalfront by wasting processing time and memory. This may be mitigated bybuilding multiple versions of an object (LOD, or level-of-detail) sothat a system may select a version and optimize time and/or memory.However, that approach still requires expending the maximum effort tocreate the most detailed version, even if lesser versions are derivedprocedurally.

SUMMARY

Embodiments generally relate to multi-angle screen coverage analysis. Invarious embodiments, instead of culling a fully built animation scenewith the highest detail level, a system determines what asset detaillevel is needed for particular objects in a scene for animators andinforms the animators in advance and over the course of a visualproduction. In some embodiments, a system obtains at least one image,where the image is a computer graphics generated image, where the imagecomprises multiple objects, and where the objects include the at leastone target object. The system then determines screen coverageinformation for the target object, where the screen coverage informationis based on a percentage of the screen that is covered by the targetobject. The system also determines depth information for the targetobject, where the depth information for the target object is based on atleast one other object. The system then determines an asset detail levelfor the target object based on the screen coverage information and thedepth information, and stores the asset detail level in a database.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example a computer graphics generatedimage, which may be used for embodiments described herein.

FIG. 2 is an example flow diagram for multi-angle screen coverageanalysis, according to some embodiments.

FIG. 3 shows basic components of an example computer system suitable foruse with embodiments described herein.

FIG. 4 is an block diagram of an example visual content generationsystem, which may be used to generate imagery in the form of stillimages and/or video sequences of images, according to some embodiments.

FIG. 5 is a block diagram of an example computer system, which may beused for embodiments described herein.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments provide multi-angle screen coverage analysis. In someembodiments, a system obtains a computer graphics generated image havingat least one target object for analysis. The system determines screencoverage information and depth information for the target object. Invarious embodiments, the depth information may include the distancebetween the target object and given camera. The system then determinesan asset detail level for the target object based on the screen coverageinformation and the depth information. The system then stores the assetdetail level in a database, and makes the asset detail level availableto users. The terms asset and target object may be used interchangeably.

In contrast to conventional solutions, the system does not build new andunique 3-dimensional (3D) sets for each shot in a visual production suchas a movie. Instead, as described in more detail herein, in variousembodiments, the system builds a master layout with assets or objectshaving flexible asset detail levels. The system may reuse a given targetobject or collection of target objects across multiple shots of thevisual production.

Using a master layout is technically advantageous, because it is moreefficient for the system to process one master layout and target objectswith appropriate asset detail levels, as opposed to processing multiplesets with objects of high asset detail levels. In other words,embodiments described herein reduce processing requirements of thesystem. For example, in some scenarios, a given target object may appearclose to the camera. As such, higher asset detail level is appropriate.In some scenarios, a given target object may appear to be in the distantbackground relative to the camera. As such, lower asset detail level isappropriate. The particular asset detail level may vary, depending onthe particular implementation.

Using a master layout with flexible asset detail levels of one or moretarget objects is also technically advantageous, because it reducesprocessing requirements of other visual production systems (e.g.,systems involving animation, lighting, etc.). These processingrequirements may involve memory, processor power, and time. Furthermore,these other visual production systems need not wait until work on thelayout and camera work is finalized. This shortens the overall visualproduction, which reduces production costs.

FIG. 1 is a block diagram of an example a computer graphics generatedimage 100, which may be used for embodiments described herein. Shown isa target object 102 and another object 104 in a scene or shot capturedin the image 100. For ease of illustration, embodiments described hereinare described in the context of target object 102. These embodiments mayalso apply to object 104 if deemed a target object.

Image 100 may be an image for any given visual production (e.g., movieproduction, video production, etc.). In this example, target object 102is a barrel. While some embodiments are described in the context of abarrel, the target object may be any type of object (e.g., car, tree,building, monster, etc.).

In various embodiments, the processing described herein regardingobjects in an image may be performed on any suitable type of image. Insome embodiments, an image may include a photo, a visual image, aderived image such as an object ID pass, etc. In various embodiments,for example, an image may be of the type variously referred to as an“object ID,” an “object ID pass,” a “render ID,” a “material ID,” etc.,and may be associated with various categories of “color maps.” Invarious embodiments, these images reduce different object models ormaterials in a scene to corresponding flat-colored “masks.” In variousembodiments, one or more masks in a scene may be subjected to one ormore of the steps described herein to arrive at a suggested detail levelfor rendering the object corresponding to the object ID represented bythe mask.

As shown, target object 102 is substantially in the middle of the imageand is shown next to another object 104 to provide some perspective.Depending on the particular shot, in some scenarios, target object 102may be in the distant background of the image as indicted by 102′. Inother scenarios, target object 102 may be closer in the foreground ofthe image as indicted by 102″.

For ease of illustration, embodiments are described herein in thecontext of a single target object 102. These embodiments also apply tomultiple target objects. There may be scenarios where the target objectvariations 102, 102′, and 102″ shown are three different barrels used inthe same frame of image 100. For example, there may be three barrels ina given image, where one barrel is close, one barrel is farther in thedistance, and one barrel is in the far distance.

In various embodiments, the system analyses the same asset or targetobject from multiple different camera angles and distances yielding arepresentative sample of the expected appearance of that assetthroughout the production. In various embodiments, the system analyzescombined samples to determine an asset detail level that artists shouldtarget when generating components of the asset (e.g., geometry,textures, etc.), thus knowing how that asset will used throughout theproduction.

In various embodiments, the asset detail level may include an amount ofimage detail. In various embodiments, the asset detail level may includeimage resolution. In various embodiments, the asset detail level may bedefined in an absolute way. For example, the asset detail level may beexpressed as a size-on-screen using a pixel count by area or dimension.In various embodiments, this count may be rounded or approximated inorder to categorize the object's detail level into an abstraction moreuseful for human interaction, communication, and selection (e.g., “256,”“512,” “1 k,” “2 k,” 4 k,” etc.).

In various embodiments, the asset detail level may be defined in arelative way. For example, the asset detail level may be expressed as apercentage or fractional size of the final image size. In variousembodiments, the asset detail level may be defined in an abstract way.For example, the asset detail level may be expressed as “low,” “medium,”“high,” etc. In various embodiments, the asset detail level is expressedto artists, technicians, and managers who have a shared understanding ofthose terms through external experience, policy, documentation,nomenclature, etc.

In various embodiments, the asset detail level is one consideration thatinforms the artists, technicians, and managers how they may apply theircraft. The exact implementation of a given asset to a given asset detaillevel specification may vary based on additional expertise and contextknown to those artists, technicians, and managers. For example, for agiven asset detail level, one asset may be required to achieve itsdetail from geometric density with little or no texture density whereasthe opposite may be true for a second asset.

Furthermore, in various embodiments, the asset detail level may includeconsideration of depth, screen space coverage, visibility, motion blur,lens blur, texture, texture resolution, native resolution of an asset,pixel density, texture density, model density, etc. The asset detaillevel may influence how much detail (e.g., texture, etc.) is visible ina given image. This influences the amount of computation resources aswell as the amount of human resources to allocate to creating assetsduring production.

Also shown is a 2D bounding box 106. In various embodiments, the systemgenerates 2D bounding box 106 such that it closely surrounds targetobject 102. As such, 2D bounding box 106 approximates the size of targetobject 102 for subsequent processing.

In various embodiments, the system generates a 2D bounding box for eachtarget object in an image. For example, in the scenario where image 100also includes target objects 102′ and 102″, the system would alsogenerate 2D bounding boxes 106′ and 106″ for respective target objects102′ and 102″. Similarly, in some embodiments, if object 104 where atarget object, the system generates a 2D bounding box 106 for object104.

For ease of illustration, while some embodiments are described in thecontext of a single target object, the system may analyze the visualfield of the different target objects in a given image. Exampleembodiments involving image 100 and its content objects are described inmore detail below.

FIG. 2 is an example flow diagram for multi-angle screen coverageanalysis, according to some implementations. Referring to both FIGS. 1and 2 , a method is initiated at block 202, where a system obtains atleast one image. In various embodiments, the image is a computergraphics generated image that includes the target object or objects.While some embodiments are described herein in the context of a singletarget object, these embodiments may apply to each target object ofmultiple target objects or grouped target objects, etc.

In various embodiments, the system obtains the image from stock footagefrom cameras and may include 3D geometry and camera information. Fromthe stock footage, the system determines the target objects in theimage. In various embodiments, the system may run a render andsimultaneously use the output of that render for various purposes,depending on the particular implementation.

At block 204, the system determines screen coverage information for thetarget object. In various embodiments, the target object is at leastpartially visible in the image. For example, in one scenario, targetobject 102 is fully visible. In another scenario, target object 102″ maybe partially visible in the image.

In various embodiments, the screen coverage information is based on apercentage, a ratio, and/or the absolute portion of the screen that iscovered by the target object. In various embodiments, the systemdetermines and compares the measurements of each 2D bounding box to therest of the frame or image 100. The terms frame and image may be usedinterchangeably. The system may determine a percentage or a ratio of thesize of each target object relative to image 100. In variousembodiments, the system may utilize a renderer such as open graphicslibrary (OpenGL), etc. to provide ID passes and to determine suchmeasurements, and may utilize other means to perform geometriccalculations of object/object and object/camera relationships.

In some embodiments, for each 2D bounding box, the system determineswhere the 2D bounding box starts and stops on both the x-axis andy-axis, whether a given object is fully visible in the image orpartially visible (partially off screen).

In various embodiments, the system provides dimensions of the 2Dbounding box relative to a given image. The system determines suchrelative dimensions for all target objects in the image. In variousembodiments, the dimensions for a given 2D bounding box are based on apercentage or ratio of the size of the 2D bounding box compared to theimage. The system may subsequently convert these dimensions to absolutepixel sizes need for further processing.

In some embodiments, the system samples pixels in the image in order todetermine the screen coverage and depth information for the targetimages as well as other 3D elements that are visible in the image frame.In some embodiments, the system may determine 3D pixel dimensions basedon the longest 2D dimension according to sampled pixels. One of thereasons for running a render, rather than performing a more basictrigonometric analysis, is that the system may require knowledge of theassigned surface materials. This knowledge informs the system whenconsidering objects behind and/or obscured by the current object. Insome embodiments, the system does not gather data for objects behind asolid wall, but still gathers data for objects visible through a windowin that wall.

In some embodiments, when considering data for objects behind aforeground object such as a solid wall, for example, the system mayconsider surface materials (which implies a render). For example, insome embodiments, a “window” may be implemented within a “wall” objectto be analyzed by making a section of the geometry transparent. This mayrequire surface shading and deformation. In various embodiments, suchmaterials may describe glass or other materials. Alternatively, in someembodiments, a wall object may have a complex topology such as a holethat is cut into the geometry. This may be achieved without shading.Without implying a limitation on the possible methods used to describean object's shape and material, in some embodiments, the system may useeither or both methods within a renderer or other means of analysis toinform the system's results.

At block 206, the system determines depth information for the at leastone target object. In various embodiments, the depth information mayinclude the distance between the target object and given camera. In someembodiments, the depth information may affect the detail level of agiven target object. For example, if given a target object such as abuilding, mountain, etc., that is rendered in the far distance, thesystem may render such an object with lower detail level and/or blurredwith less detail, as more detail would not be necessary.

In various embodiments, a given image may have multiple objects,including one or more target objects. The system may determine the depthinformation for the target object based on one or more of the otherobjects in the image. For example, the system may determine the depth oftarget object 102 relative to object 104. In various embodiments, thesystem may already have depth information of object 104 and sizeinformation about object 104 (e.g., dimensions, etc.) stored in adatabase. In various embodiments, the system may also have a known sizeand position of object 104 in the particular scene, which may be storedin the database. In various embodiments, the system may also know sizeinformation about target object 102 (e.g., dimensions, etc.) stored inthe database. In various embodiments, the system may ascertain depthinformation for target object 102 relative to the known information onobject 104 or other objects that may appear in image 100.

At block 208, the system determines an asset detail level for the targetobject based on the screen coverage information and the depthinformation. As such, embodiments enable a given view of a target objectto significantly change at any point over the course of visualproduction (e.g., film/movie production, video production, etc.).Embodiments match the effort involved in creating those objects with themanner in which the objects will appear in the final version of thevisual production. For example, distant objects may have lower assetdetail level, and closer objects have higher asset detail level, etc.

At block 210, the system stores the asset detail level in a database. Invarious embodiments, the system outputs an asset detail level for targetobject 102 (and for each target object). In some embodiments, the assetdetail level may be a pixel value. For example, if the image frame is2,000 pixels wide, and the longest length of the 2D bounding box is1,000 pixels, the pixel resolution may be 1,000 pixels. The system mayadjust the asset detail level based on the actual image frame size(2,000 or 2K image, 4,000 or 4K image, etc.).

In various embodiments, the system provides a holistic analysis of shotcontent over the course of shot production, and provides informationfrom the analysis to other users to inform decisions along the way. Insome embodiments, the system may send or provide the asset detail levelof target object 102 as well as the asset detail level of other targetobjects to one or more users. Also, the system makes asset detail levelavailable to various users (e.g., artists, productions managers, etc.)for making decisions based on the desired level of detail of the targetobjects.

In some embodiments, the system schedules or enables the scheduling ofartistic work on the target object or a collection of target objects. Assuch, down-stream systems of the visual production that consume thosetarget objects may proceed as early as possible, or as desired byproduction management.

Although the steps, operations, or computations may be presented in aspecific order, the order may be changed in particular implementations.Other orderings of the steps are possible, depending on the particularimplementation. In some particular implementations, multiple steps shownas sequential in this specification may be performed at the same time.Also, some implementations may not have all of the steps shown and/ormay have other steps instead of, or in addition to, those shown herein.

The following describes additional embodiments. Embodiments facilitateartists and production managers to make decisions around how much effortto expend on a given object, and when to schedule that effort. This canbe used at any point in the production process from previsualizationscenes with inexpensive blocking versions of the asset (aka, first-lookor stand-in versions) to final or near-final quality versions expectedto be in the final images. Tracking this information over the life ofthe movie production may help highlight unexpected changes or predictthe cost of intended changes.

In various embodiments, the system replicates the data set associatedwith the target object, including the asset detail level across numerousframes (e.g., 200K frames, etc.). The system identifies and tracks eachtarget object across the different frames. In various embodiments, thesystem tracks the identity of a given target such as target object 102with a name and identifier (e.g., “Barrel 24”). For example, in someembodiments, the target object may appear in the scene at one moment(e.g., in different frames), leave the scene at a subsequent moment(e.g., leaves the different frames), and then return to the scene inanother subsequent moment (e.g., reenters the different frames).

In various embodiments, the system may utilize any suitable renderer tocollect screen coverage and depth information for each and every targetobject, which may include other logical elements in a given image. Thesystem may also serialize the target object and corresponding assetdetail level, and store this information in a database. In variousembodiments, the system may analyze a selected sample of frames in agiven shot to collect reasonably complete data, without rendering eachand every frame. This is technically advantageous in that the systemprocesses fewer frames.

In various embodiments, the system may perform statistical analysis onmultiple frames and across multiple shots in a given visual production.For example, in some embodiments, the system may perform statisticalanalysis of the data across the range of frames in the shot. The systemmay also condense the per-element data (e.g., barrel 1, barrel 2, barrel3, etc.) down to the unique asset (barrel).

In various embodiments, the system may collate the results from multipleframes and across multiple shots in a given visual production. Forexample, in some embodiments, the system may store and collate with datamultiple shots in a database, which can be made available via othersystems for a variety of tasks. In some embodiments, the asset detaillevel may be made available to and facilitate production managers indeciding how much effort to put into a given asset (e.g., targetobject).

In various embodiments, the system may assemble lists of assets ortarget objects visible in each shot. This facilitates productionschedule work on those assets in such a way as to free downstreamdepartments to commence work as early as possible. In variousembodiments, the system collates information from many shots as part ofthe production planning. For example, the system may process over 100shots and then identify the most commonly seen assets. In anotherexample, the system may determine the least costly assets to create, andthe system may prioritize work on those assets.

In various embodiments, the system may facilitate production managers,technicians, etc. in designing layouts by helping choose an appropriatetarget object (or version of the target object) that is neither tooexpensive to render in the background nor too simple to be used in theforeground.

In various embodiments, the system may identify when to modify a layoutand/or camera as needed, such as when a completed asset is to be usedoutside its build specifications.

In various embodiments, the system may combine the asset detail level ofa given target object with image sequence information (ISI) data toselectively load and inspect portions of the 3D scene from a 2D movie.In various embodiments, an ISI data is used by image/movie review tools,which allow a user to point and click on an object in the image and seeadditional information about that image (e.g., name; ID; variants suchas “redshirt,” “stripedshirt,” etc.; and/or other data as determined andpopulated by the system).

In various embodiments, the system has one asset (e.g., one barrelasset) that is used multiple times across the frames. For example,target object 102 may be a barrel asset that is used for differentbarrels (e.g., barrels 25, 27, 32, etc.) in different shots, includingdifferent shots in different visual productions.

In various embodiments, the system may have information for each of thebarrels, collate the information, and store the information under oneunique asset. In this example, target object 102 may be referred to asthe barrel asset that may be reused at different distances and angles indifferent frames. As such, target object 102 may be used at the positionof target object 102′, at the position of target object 102″, etc.

This provides the maximum size that the barrel asset will ever show up(e.g., target object 102″), the minimum size that the barrel asset willever show up (e.g., target object 102′), and an average size that thebarrel asset will ever show up (e.g., target object 102).

In various embodiments, the system uses the maximum and minimum sizes topredict future sizes in future frames yet to be determined. For example,maximum and minimum size of target object 102 may be ascertained at somepoint during preproduction, and either or both of maximum and minimumsize of target object 102 may change at any point during preproductionor during postproduction. In various embodiments, the system iterativelyadjusts such maximum and minimum sizes accordingly, as the system comesinto possession of new data from new cameras, new sequences, etc. In anexample scenario, a camera may be subsequently adjusted to make a giventarget object appear at a much closer distance. As such, the system mayupdate the maximum size of the target object.

While some embodiments are described in the context of the size of thetarget object, these embodiments and others may also apply to otheraspects of the asset. Such aspects may include a depth number (e.g.,distance from a camera), texture, color, etc.

As indicated herein, when a given set for a visual production iscreated, there may be thousands or even tens of thousands of objects ina given scene/set and related frames. The layout may change with newdressing such as trees, shrubs, animals, etc.

In some embodiments, the same target object (e.g., barrel asset) mayhave different traits or variances in details (e.g., color, texture,etc.). As such, the same asset may be used for a red barrel, a bluebarrel, etc.

Such variances may also include wardrobe. For example, a given personmay not be wearing glasses and a hat in one scene and may be wearingglasses and a hat in a subsequent scene. In various embodiments, thesystem may identify and track each of these wardrobe accessories asindividual, separate assets.

In various embodiments, the system continuously aggregates theinformation over the time of the production as new information islearned. The system updates this information in a database.

In various embodiments, the system may inform management when changes toaggregate information are learned about, as in when a new maximum orminimum size is learned about and the new size exceeds the asset's buildspecification. In various embodiments, the system may send a message toproduction indicating a layout or camera that causes an asset to falloutside the specification for which production has planned and budgeted.

In some embodiments, the system may model and render a given object fromdifferent distances, perspectives, etc., as would be seen in a movie.This may help production managers to make decisions based on viewedobjects with particular levels of detail.

In some embodiments, the system may provide a user interface to displayinformation generated herein (e.g., asset detail level, example renderedobjects in images, etc.) to a user.

In some embodiments, the system may utilize or augment an existingdisplay or in-house renderer with additional information generatedherein, (e.g., color coding the objects to convey information.) In someembodiments, the system may utilize an openGL shader in an existingdisplay to statically color code objects, or to dynamically color codeobjects according to a comparison between predetermined asset detaillevel and current placement of the object (e.g., make the object red ifit is too close to camera, etc.).

Embodiments described herein may be plugged directly into in-houserenderers to reduce the need to run special processes to generate data.Embodiments may account for requested specifications or actualspecifications.

Embodiments described herein provide various benefits. For example,instead of culling a fully built animation scene with the highest detaillevel, the system determines what asset detail level is needed in partsof the scene for animators and informs animators in advance.

FIG. 3 is a block diagram of an exemplary computer system 300 for usewith implementations described herein. Computer system 300 is merelyillustrative and not intended to limit the scope of the claims. One ofordinary skill in the art would recognize other variations,modifications, and alternatives. For example, computer system 300 may beimplemented in a distributed client-server configuration having one ormore client devices in communication with one or more server systems.

In one exemplary implementation, computer system 300 includes a displaydevice such as a monitor 310, computer 320, a data entry interface 330such as a keyboard, touch device, and the like, a user input device 340,a network communication interface 350, and the like. User input device340 is typically embodied as a computer mouse, a trackball, a track pad,wireless remote, tablet, touch screen, and the like. Moreover, userinput device 340 typically allows a user to select and operate objects,icons, text, characters, and the like that appear, for example, on themonitor 310.

Network interface 350 typically includes an Ethernet card, a modem(telephone, satellite, cable, ISDN), (asynchronous) digital subscriberline (DSL) unit, and the like. Further, network interface 350 may bephysically integrated on the motherboard of computer 320, may be asoftware program, such as soft DSL, or the like.

Computer system 300 may also include software that enablescommunications over communication network 352 such as the HTTP, TCP/IP,RTP/RTSP, protocols, wireless application protocol (WAP), IEEE 902.11protocols, and the like. In addition to and/or alternatively, othercommunications software and transfer protocols may also be used, forexample IPX, UDP or the like. Communication network 352 may include alocal area network, a wide area network, a wireless network, anIntranet, the Internet, a private network, a public network, a switchednetwork, or any other suitable communication network, such as forexample Cloud networks. Communication network 352 may include manyinterconnected computer systems and any suitable communication linkssuch as hardwire links, optical links, satellite or other wirelesscommunications links such as BLUETOOTH, WIFI, wave propagation links, orany other suitable mechanisms for communication of information. Forexample, communication network 352 may communicate to one or more mobilewireless devices 356A-N, such as mobile phones, tablets, and the like,via a base station such as wireless transceiver 354.

Computer 320 typically includes familiar computer components such as aprocessor 360, and memory storage devices, such as a memory 370, e.g.,random access memory (RAM), storage media 380, and system bus 390interconnecting the above components. In one embodiment, computer 320 isa PC compatible computer having multiple microprocessors, graphicsprocessing units (GPU), and the like. While a computer is shown, it willbe readily apparent to one of ordinary skill in the art that many otherhardware and software configurations are suitable for use with thepresent invention. Memory 370 and Storage media 380 are examples oftangible non-transitory computer readable media for storage of data,audio/video files, computer programs, and the like. Other types oftangible media include disk drives, solid-state drives, floppy disks,optical storage media and bar codes, semiconductor memories such asflash drives, flash memories, random-access or read-only types ofmemories, battery-backed volatile memories, networked storage devices,Cloud storage, and the like.

FIG. 4 is an block diagram of an example visual content generationsystem 400, which may be used to generate imagery in the form of stillimages and/or video sequences of images, according to some embodiments.The visual content generation system 400 might generate imagery of liveaction scenes, computer generated scenes, or a combination thereof. In apractical system, users are provided with tools that allow them tospecify, at high levels and low levels where necessary, what is to gointo that imagery. For example, a user might be an animation artist andmight use the visual content generation system 400 to captureinteraction between two human actors performing live on a sound stageand replace one of the human actors with a computer-generatedanthropomorphic non-human being that behaves in ways that mimic thereplaced human actor's movements and mannerisms, and then add in a thirdcomputer-generated character and background scene elements that arecomputer-generated, all in order to tell a desired story or generatedesired imagery.

Still images that are output by the visual content generation system 400might be represented in computer memory as pixel arrays, such as atwo-dimensional array of pixel color values, each associated with apixel having a position in a two-dimensional image array. Pixel colorvalues might be represented by three or more (or fewer) color values perpixel, such as a red value, a green value, and a blue value (e.g., inRGB format). Dimensions of such a two-dimensional array of pixel colorvalues might correspond to a preferred and/or standard display scheme,such as 1920 pixel columns by 1280 pixel rows. Images might or might notbe stored in a compressed format, but either way, a desired image may berepresented as a two-dimensional array of pixel color values. In anothervariation, images are represented by a pair of stereo images forthree-dimensional presentations and in other variations, some or all ofan image output might represent three-dimensional imagery instead ofjust two-dimensional views.

A stored video sequence might include a plurality of images such as thestill images described above, but where each image of the plurality ofimages has a place in a timing sequence, and the stored video sequenceis arranged so that when each image is displayed in order, at a timeindicated by the timing sequence, the display presents what appears tobe moving and/or changing imagery. In one representation, each image ofthe plurality of images is a video frame having a specified frame numberthat corresponds to an amount of time that would elapse from when avideo sequence begins playing until that specified frame is displayed. Aframe rate might be used to describe how many frames of the stored videosequence are displayed per unit time. Example video sequences mightinclude 24 frames per second (24 FPS), 50 FPS, 80 FPS, or other framerates. In some embodiments, frames are interlaced or otherwise presentedfor display, but for the purpose of clarity of description, in someexamples, it is assumed that a video frame has one specified displaytime and it should be understood that other variations are possible.

One method of creating a video sequence is to simply use a video camerato record a live action scene, i.e., events that physically occur andcan be recorded by a video camera. The events being recorded can beevents to be interpreted as viewed (such as seeing two human actors talkto each other) and/or can include events to be interpreted differentlydue to clever camera operations (such as moving actors about a stage tomake one appear larger than the other despite the actors actually beingof similar build, or using miniature objects with other miniatureobjects so as to be interpreted as a scene containing life-sizedobjects).

Creating video sequences for story-telling or other purposes often callsfor scenes that cannot be created with live actors, such as a talkingtree, an anthropomorphic object, space battles, and the like. Such videosequences might be generated computationally rather than capturing lightfrom live scenes. In some instances, an entirety of a video sequencemight be generated computationally, as in the case of acomputer-animated feature film. In some video sequences, it is desirableto have some computer-generated imagery and some live action, perhapswith some careful merging of the two.

While computer-generated imagery might be creatable by manuallyspecifying each color value for each pixel in each frame, this is likelytoo tedious to be practical. As a result, a creator uses various toolsto specify the imagery at a higher level. As an example, an artist mightspecify the positions in a scene space, such as a three-dimensionalcoordinate system, might specify positions of objects and/or lighting,as well as a camera viewpoint, and a camera view plane. Taking all ofthose as inputs, a rendering engine may compute each of the pixel valuesin each of the frames. In another example, an artist specifies positionand movement of an articulated object having some specified texturerather than specifying the color of each pixel representing thatarticulated object in each frame.

In a specific example, a rendering engine performs ray tracing wherein apixel color value is determined by computing which objects lie along aray traced in the scene space from the camera viewpoint through a pointor portion of the camera view plane that corresponds to that pixel. Forexample, a camera view plane might be represented as a rectangle havinga position in the scene space that is divided into a grid correspondingto the pixels of the ultimate image to be generated. In the example, aray defined by the camera viewpoint in the scene space and a given pixelin that grid first intersects a solid, opaque, blue object, that givenpixel is assigned the color blue. Of course, for modemcomputer-generated imagery, determining pixel colors, and therebygenerating imagery, can be more complicated, as there are lightingissues, reflections, interpolations, and other considerations.

In various embodiments, a live action capture system 402 captures a livescene that plays out on a stage 404. The live action capture system 402is described herein in greater detail, but might include computerprocessing capabilities, image processing capabilities, one or moreprocessors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown.

In a specific live action capture system, cameras 406(1) and 406(2)capture the scene, while in some systems, there might be other sensor(s)408 that capture information from the live scene (e.g., infraredcameras, infrared sensors, motion capture (“mo-cap”) detectors, etc.).On the stage 404, there might be human actors, animal actors, inanimateobjects, background objects, and possibly an object such as a greenscreen 410 that is designed to be captured in a live scene recording insuch a way that it is easily overlaid with computer-generated imagery.The stage 404 might also contain objects that serve as fiducials, suchas fiducials 412(1)-(3), that might be used post-capture to determinewhere an object was during capture. A live action scene might beilluminated by one or more lights, such as an overhead light 414.

During or following the capture of a live action scene, the live actioncapture system 402 might output live action footage to a live actionfootage storage 420. A live action processing system 422 might processlive action footage to generate data about that live action footage andstore that data into a live action metadata storage 424. The live actionprocessing system 422 might include computer processing capabilities,image processing capabilities, one or more processors, program codestorage for storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown. The live action processing system 422 mightprocess live action footage to determine boundaries of objects in aframe or multiple frames, determine locations of objects in a liveaction scene, where a camera was relative to some action, distancesbetween moving objects and fiducials, etc. Where elements are detectedby sensor or other means, the metadata might include location, color,and intensity of the overhead light 414, as that might be useful inpost-processing to match computer-generated lighting on objects that arecomputer-generated and overlaid on the live action footage. The liveaction processing system 422 might operate autonomously, perhaps basedon predetermined program instructions, to generate and output the liveaction metadata upon receiving and inputting the live action footage.The live action footage can be camera-captured data as well as data fromother sensors.

An animation creation system 430 is another part of the visual contentgeneration system 400. The animation creation system 430 might includecomputer processing capabilities, image processing capabilities, one ormore processors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown. The animationcreation system 430 might be used by animation artists, managers, andothers to specify details, perhaps programmatically and/orinteractively, of imagery to be generated. From user input and data froma database or other data source, indicated as a data store 432, theanimation creation system 430 might generate and output datarepresenting objects (e.g., a horse, a human, a ball, a teapot, a cloud,a light source, a texture, etc.) to an object storage 434, generate andoutput data representing a scene into a scene description storage 436,and/or generate and output data representing animation sequences to ananimation sequence storage 438.

Scene data might indicate locations of objects and other visualelements, values of their parameters, lighting, camera location, cameraview plane, and other details that a rendering engine 450 might use torender CGI imagery. For example, scene data might include the locationsof several articulated characters, background objects, lighting, etc.specified in a two-dimensional space, three-dimensional space, or otherdimensional space (such as a 2.5-dimensional space, three-quarterdimensions, pseudo-3D spaces, etc.) along with locations of a cameraviewpoint and view place from which to render imagery. For example,scene data might indicate that there is to be a red, fuzzy, talking dogin the right half of a video and a stationary tree in the left half ofthe video, all illuminated by a bright point light source that is aboveand behind the camera viewpoint. In some cases, the camera viewpoint isnot explicit, but can be determined from a viewing frustum. In the caseof imagery that is to be rendered to a rectangular view, the frustumwould be a truncated pyramid. Other shapes for a rendered view arepossible and the camera view plane could be different for differentshapes.

The animation creation system 430 might be interactive, allowing a userto read in animation sequences, scene descriptions, object details, etc.and edit those, possibly returning them to storage to update or replaceexisting data. As an example, an operator might read in objects fromobject storage into a baking processor that would transform thoseobjects into simpler forms and return those to the object storage 434 asnew or different objects. For example, an operator might read in anobject that has dozens of specified parameters (movable joints, coloroptions, textures, etc.), select some values for those parameters andthen save a baked object that is a simplified object with now fixedvalues for those parameters.

Rather than have to specify each detail of a scene, data from the datastore 432 might be used to drive object presentation. For example, if anartist is creating an animation of a spaceship passing over the surfaceof the Earth, instead of manually drawing or specifying a coastline, theartist might specify that the animation creation system 430 is to readdata from the data store 432 in a file containing coordinates of Earthcoastlines and generate background elements of a scene using thatcoastline data.

Animation sequence data might be in the form of time series of data forcontrol points of an object that has attributes that are controllable.For example, an object might be a humanoid character with limbs andjoints that are movable in manners similar to typical human movements.An artist can specify an animation sequence at a high level, such as“the left hand moves from location (X1, Y1, Z1) to (X2, Y2, Z2) overtime T1 to T2”, at a lower level (e.g., “move the elbow joint 2.5degrees per frame”) or even at a very high level (e.g., “character Ashould move, consistent with the laws of physics that are given for thisscene, from point P1 to point P2 along a specified path”).

Animation sequences in an animated scene might be specified by whathappens in a live action scene. An animation driver generator 444 mightread in live action metadata, such as data representing movements andpositions of body parts of a live actor during a live action scene, andgenerate corresponding animation parameters to be stored in theanimation sequence storage 438 for use in animating a CGI object. Thiscan be useful where a live action scene of a human actor is capturedwhile wearing mo-cap fiducials (e.g., high-contrast markers outsideactor clothing, high-visibility paint on actor skin, face, etc.) and themovement of those fiducials is determined by the live action processingsystem 422. The animation driver generator 444 might convert thatmovement data into specifications of how joints of an articulated CGIcharacter are to move over time.

A rendering engine 450 can read in animation sequences, scenedescriptions, and object details, as well as rendering engine controlinputs, such as a resolution selection and a set of renderingparameters. Resolution selection might be useful for an operator tocontrol a trade-off between speed of rendering and clarity of detail, asspeed might be more important than clarity for a movie maker to test aparticular interaction or direction, while clarity might be moreimportant than speed for a movie maker to generate data that will beused for final prints of feature films to be distributed. The renderingengine 450 might include computer processing capabilities, imageprocessing capabilities, one or more processors, program code storagefor storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown.

The visual content generation system 400 can also include a mergingsystem 460 (labeled “Live+CGI Merging System”) that merges live footagewith animated content. The live footage might be obtained and input byreading from the live action footage storage 420 to obtain live actionfootage, by reading from the live action metadata storage 424 to obtaindetails such as presumed segmentation in captured images segmentingobjects in a live action scene from their background (perhaps aided bythe fact that the green screen 410 was part of the live action scene),and by obtaining CGI imagery from the rendering engine 450.

A merging system 460 might also read data from rule sets formerging/combining storage 462. A very simple example of a rule in a ruleset might be “obtain a full image including a two-dimensional pixelarray from live footage, obtain a full image including a two-dimensionalpixel array from the rendering engine 450, and output an image whereeach pixel is a corresponding pixel from the rendering engine 450 whenthe corresponding pixel in the live footage is a specific color ofgreen, otherwise output a pixel value from the corresponding pixel inthe live footage.”

The merging system 460 might include computer processing capabilities,image processing capabilities, one or more processors, program codestorage for storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown. The merging system 460 might operateautonomously, following programming instructions, or might have a userinterface or programmatic interface over which an operator can control amerging process. In some embodiments, an operator can specify parametervalues to use in a merging process and/or might specify specific tweaksto be made to an output of the merging system 460, such as modifyingboundaries of segmented objects, inserting blurs to smooth outimperfections, or adding other effects. Based on its inputs, the mergingsystem 460 can output an image to be stored in a static image storage470 and/or a sequence of images in the form of video to be stored in ananimated/combined video storage 472.

Thus, as described, the visual content generation system 400 can be usedto generate video that combines live action with computer-generatedanimation using various components and tools, some of which aredescribed in more detail herein. While the visual content generationsystem 400 might be useful for such combinations, with suitablesettings, it can be used for outputting entirely live action footage orentirely CGI sequences. The code may also be provided and/or carried bya transitory computer readable medium, e.g., a transmission medium suchas in the form of a signal transmitted over a network.

According to one embodiment, the techniques described herein areimplemented by one or more generalized computing systems programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Special-purpose computingdevices may be used, such as desktop computer systems, portable computersystems, handheld devices, networking devices or any other device thatincorporates hard-wired and/or program logic to implement thetechniques.

FIG. 5 is a block diagram of an example computer system 500, which maybe used for embodiments described herein. The computer system 500includes a bus 502 or other communication mechanism for communicatinginformation, and a processor 504 coupled with the bus 502 for processinginformation. The processor 504 may be, for example, a general purposemicroprocessor.

The computer system 500 also includes a main memory 506, such as arandom access memory (RAM) or other dynamic storage device, coupled tothe bus 502 for storing information and instructions to be executed bythe processor 504. The main memory 506 may also be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by the processor 504. Such instructions,when stored in non-transitory storage media accessible to the processor504, render the computer system 500 into a special-purpose machine thatis customized to perform the operations specified in the instructions.

The computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to the bus 502 for storing staticinformation and instructions for the processor 504. A storage device510, such as a magnetic disk or optical disk, is provided and coupled tothe bus 502 for storing information and instructions.

The computer system 500 may be coupled via the bus 502 to a display 512,such as a computer monitor, for displaying information to a computeruser. An input device 514, including alphanumeric and other keys, iscoupled to the bus 502 for communicating information and commandselections to the processor 504. Another type of user input device is acursor control 516, such as a mouse, a trackball, or cursor directionkeys for communicating direction information and command selections tothe processor 504 and for controlling cursor movement on the display512. This input device 514 typically has two degrees of freedom in twoaxes, a first axis (e.g., x) and a second axis (e.g., y), that allowsthe input device 514 to specify positions in a plane.

The computer system 500 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmware,and/or program logic which, in combination with the computer system,causes or programs the computer system 500 to be a special-purposemachine. According to one embodiment, the techniques herein areperformed by the computer system 500 in response to the processor 504executing one or more sequences of one or more instructions contained inthe main memory 506. Such instructions may be read into the main memory506 from another storage medium, such as the storage device 510.Execution of the sequences of instructions contained in the main memory506 causes the processor 504 to perform the process steps describedherein. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may includenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as the storage device 510.Volatile media includes dynamic memory, such as the main memory 506.Common forms of storage media include, for example, a floppy disk, aflexible disk, hard disk, solid state drive, magnetic tape, or any othermagnetic data storage medium, a CD-ROM, any other optical data storagemedium, any physical medium with patterns of holes, a RAM, a PROM, anEPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire, and fiber optics, including thewires that include the bus 502. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to the processor 504 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over anetwork connection. A modem or network interface local to the computersystem 500 can receive the data. The bus 502 carries the data to themain memory 506, from which the processor 504 retrieves and executes theinstructions. The instructions received by the main memory 506 mayoptionally be stored on the storage device 510 either before or afterexecution by the processor 504.

The computer system 500 also includes a communication interface 518coupled to the bus 502. The communication interface 518 provides atwo-way data communication coupling to a network link 520 that isconnected to a local network 522. For example, the communicationinterface 518 may be an integrated services digital network (“ISDN”)card, cable modem, satellite modem, or a modem to provide a datacommunication connection to a corresponding type of telephone line.Wireless links may also be implemented. In any such implementation, thecommunication interface 518 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

The network link 520 typically provides data communication through oneor more networks to other data devices. For example, the network link520 may provide a connection through a local network 522 to a hostcomputer 524 or to data equipment operated by an Internet ServiceProvider (“ISP”) 526. The ISP 526 in turn provides data communicationservices through the world wide packet data communication network nowcommonly referred to as the “Internet” 528. The local network 522 andthe Internet 528 both use electrical, electromagnetic, or opticalsignals that carry digital data streams. The signals through the variousnetworks and the signals on the network link 520 and through thecommunication interface 518, which carry the digital data to and fromthe computer system 500, are example forms of transmission media.

The computer system 500 can send messages and receive data, includingprogram code, through the network(s), the network link 520, and thecommunication interface 518. In the Internet example, a server 530 mighttransmit a requested code for an application program through theInternet 528, the ISP 526, the local network 522, and the communicationinterface 518. The received code may be executed by the processor 504 asit is received, and/or stored in the storage device 510, or othernon-volatile storage for later execution.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein may be performedunder the control of one or more computer systems (e.g., the computersystem 500) configured with executable instructions and may beimplemented as code (e.g., executable instructions, one or more computerprograms, or one or more applications) executing collectively on one ormore processors, by hardware, or combinations thereof. The code may bestored on a computer-readable storage medium, for example, in the formof a computer program including a plurality of instructions executableby one or more processors. The computer-readable storage medium may benon-transitory.

Although the description has been described with respect to particularembodiments thereof, these particular embodiments are merelyillustrative, and not restrictive. Controls can be provided to allowmodifying various parameters of the compositing at the time ofperforming the recordings. For example, the resolution, number offrames, accuracy of depth position may all be subject to human operatorchanges or selection.

Any suitable programming language can be used to implement the routinesof particular embodiments including C, C++, Java, assembly language,etc. Different programming techniques can be employed such as proceduralor object oriented. The routines can execute on a single processingdevice or multiple processors. Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different particular embodiments. In some particularembodiments, multiple steps shown as sequential in this specificationcan be performed at the same time.

Particular embodiments may be implemented in a computer-readable storagemedium for use by or in connection with the instruction executionsystem, apparatus, system, or device. Particular embodiments can beimplemented in the form of control logic in software or hardware or acombination of both. The control logic, when executed by one or moreprocessors, may be operable to perform that which is described inparticular embodiments.

Some embodiments are implemented as a non-transitory processor-readablemedium including instructions executable by one or more digitalprocessors. The processor-readable medium comprising one or moreinstructions executable by the one or more digital processors forimplementing embodiments described herein.

Some embodiments are implemented as processor implementable codeprovided on a computer-readable medium. The computer-readable medium maycomprise a non-transient storage medium, such as solid-state memory, amagnetic disk, optical disk, etc., or a transient medium such as asignal transmitted over a computer network.

Particular embodiments may be implemented by using a programmed generalpurpose digital computer, by using application specific integratedcircuits, programmable logic devices, field programmable gate arrays,optical, chemical, biological, quantum or nanoengineered systems,components and mechanisms may be used. In general, the functions ofparticular embodiments can be achieved by any means as is known in theart. Distributed, networked systems, components, and/or circuits can beused. Communication, or transfer, of data may be wired, wireless, or byany other means.

It will also be appreciated that one or more of the elements depicted inthe drawings/figures can also be implemented in a more separated orintegrated manner, or even removed or rendered as inoperable in certaincases, as is useful in accordance with a particular application. It isalso within the spirit and scope to implement a program or code that canbe stored in a machine-readable medium to permit a computer to performany of the methods described above.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise. Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

Thus, while particular embodiments have been described herein, latitudesof modification, various changes, and substitutions are intended in theforegoing disclosures, and it will be appreciated that in some instancessome features of particular embodiments will be employed without acorresponding use of other features without departing from the scope andspirit as set forth. Therefore, many modifications may be made to adapta particular situation or material to the essential scope and spirit.

I claim:
 1. A computer-implemented method performed by one or moredigital processors for multi-angle screen coverage analysis, the methodcomprising: obtaining at least one image, wherein the at least one imageis a computer graphics generated image, and wherein the at least oneimage comprises at least one target object; determining screen coverageinformation for the at least one target object; determining depthinformation for the at least one target object, wherein the depthinformation comprises a distance between the at least one target objectand at least one camera; determining an asset detail level for the atleast one target object based on the screen coverage information and thedepth information; generating an asset data set associated with the atleast one target object, wherein the asset data set comprises a model ofthe at least one target object, the depth information, and the assetdetail level; storing the asset data set in a database for subsequentreuse of the asset data set in a visual production; aggregating newinformation associated with the at least one target object over a timeof the visual production; iteratively updating the asset data set in thedatabase; retrieving the asset data set from the database; and renderingthe at least one target object based on the asset data set.
 2. Themethod of claim 1, further comprising rendering the at least one targetobject at one or more of different distances or angles in differentframes of the visual production.
 3. The method of claim 1, wherein thescreen coverage information is based on a percentage of a screen that iscovered by the at least one target object.
 4. The method of claim 1,wherein the screen coverage information is based on an absolute portionof a screen that is covered by the at least one target object.
 5. Themethod of claim 1, wherein the at least one image comprises a pluralityof objects, wherein the plurality of objects comprises the at least onetarget object, and wherein the depth information for the at least onetarget object is based on at least one other object of the plurality ofobjects.
 6. The method of claim 1, further comprising providing theasset detail level to one or more users.
 7. The method of claim 1,wherein in the asset detail level comprises an amount of image detail.8. The method of claim 1, wherein the asset detail level comprises animage resolution.
 9. An apparatus for multi-angle screen coverageanalysis, the apparatus comprising: one or more processors; and logicencoded in one or more tangible media for execution by the one or moreprocessors for: obtaining at least one image, wherein the at least oneimage is a computer graphics generated image, and wherein the at leastone image comprises at least one target object; determining screencoverage information for the at least one target object; determiningdepth information for the at least one target object, wherein the depthinformation comprises a distance between the at least one target objectand at least one camera; determining an asset detail level for the atleast one target object based on the screen coverage information and thedepth information; generating an asset data set associated with the atleast one target object, wherein the asset data set comprises a model ofthe at least one target object, the depth information, and the assetdetail level; storing the asset data set in a database for subsequentreuse of the asset data set in a visual production; aggregating newinformation associated with the at least one target object over a timeof the visual production; iteratively updating the asset data set in thedatabase; retrieving the asset data set from the database; and renderingthe at least one target object based on the asset data set.
 10. Theapparatus of claim 9, wherein the at least one target object is at leastpartially visible in the at least one image.
 11. The apparatus of claim9, wherein the at least one target object is fully visible in the atleast one image.
 12. The apparatus of claim 9, wherein the screencoverage information is based on a percentage of a screen that iscovered by the at least one target object.
 13. The apparatus of claim 9,wherein the screen coverage information is based on an absolute portionof a screen that is covered by the at least one target object.
 14. Theapparatus of claim 9, wherein the at least one image comprises aplurality of objects, wherein the plurality of objects comprises the atleast one target object, and wherein the depth information for the atleast one target object is based on at least one other object of theplurality of objects.
 15. The apparatus of claim 9, wherein the logicwhen executed is further operable to cause the one or more processors toperform operations comprising providing the asset detail level to one ormore users.
 16. The apparatus of claim 9, wherein in the asset detaillevel comprises an amount of image detail.
 17. The apparatus of claim 9,wherein the asset detail level comprises an image resolution.
 18. Anon-transitory computer-readable storage medium with programinstructions stored thereon, the program instructions when executed byone or more processors are operable to cause the one or more processorsto perform operations comprising: obtaining at least one image, whereinthe at least one image is a computer graphics generated image, andwherein the at least one image comprises at least one target object;determining screen coverage information for the at least one targetobject; determining depth information for the at least one targetobject, wherein the depth information comprises a distance between theat least one target object and at least one camera; determining an assetdetail level for the at least one target object based on the screencoverage information and the depth information; generating an asset dataset associated with the at least one target object, wherein the assetdata set comprises a model of the at least one target object, the depthinformation, and the asset detail level; storing the asset data set in adatabase for subsequent reuse of the asset data set in a visualproduction; aggregating new information associated with the at least onetarget object over a time of the visual production; iteratively updatingthe asset data set in the database; retrieving the asset data set fromthe database; and rendering the at least one target object based on theasset data set.
 19. The computer-readable storage medium of claim 18,wherein the at least one target object is at least partially visible inthe at least one image.